Robust object tracking via sparsity-based collaborative model

By Wei Zhong, Huchuan Lu, Ming-Hsuan Yang. 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012

1,031 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/fe2aaad872a2cf08c09dd52ca972f323666306db

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In Computer Vision and Pattern Recognition

Citations in the corpus

Adaptive Color Attributes for Real-Time Visual Tracking (2014, methodology): “CT [27] LSST [24] Frag [1] L1APG [2] LOT [18] ASLA [10] TLD [11] SCM [28] EDFT [6] CSK [9] DFT [20] CXT [5] CPF [19] LSHT [8] Struck [7] CN2 CN Median CLE 78.”
Robust Visual Tracking via Convolutional Networks Without Training (2015, positive): “local ﬁlters can be referred as a set of ﬁxed local templates that encode stable information in the ﬁrst frame, thereby handling the drifting problem effectively. Similar strategy has been adopted in [10], [29], [36], where [29] utilizes the template in the ﬁrst frame and the tracked result to updat”
Exploring Causal Relationships in Visual Object Tracking (2015, methodology): “We have proposed an approach to discover and quantify this relationship using Category ASLA[21] SCM[41] Struck[17] Struck StruckBMC Struckφt BC 0.”
Large Margin Object Tracking with Circulant Feature Maps (2017, background): “The ﬁrst one is to explore an effective tracking algorithm which can be designed to be either discriminative [17, 9, 13, 15, 22, 1, 31] or generative [24, 18, 16, 33] models.”
A Survey of Sparse Representation: Algorithms and Applications (2015, methodology): “[210] addressed the object tracking problem by developing a sparsit ybased collaborative model, which combined a sparsity-base d classifier learned from holistic templates and a sparsity-b ased template model generated from local representations.”
Locally Orderless Tracking (2012, methodology): “2010), Robust Object Tracking via Sparsity-based Collaborative Model (SCM) (Zhong et al. 2012) and Visual Tracking via Adaptive Structural Local Sparse Appearance Model (ASLA) (Jia et al.”
Struck: Structured Output Tracking with Kernels (2016, neutral): “Example frames from benchmark sequences, comparing the results of Struck (variant mklHGHI) with KCF [22], SCM [49] and ASLA [51].”
DeepTrack: Learning Discriminative Feature Representations by Convolutional Neural Networks for Visual Tracking (2014, methodology): “We compare our method with 9 other state-of-the-art trackers including TLD [12], CXT [6], ASLA [11], Struck [9], SCM [19], and the classical tracking algorithms, e.g., Frag [1], CPF [15], IVT [16] and MIL [2].”
Track and Segment: An Iterative Unsupervised Approach for Video Object Proposals (2016, background): “, [20, 59, 55, 48, 17]) share our goal of localizing the same object over the video, but require human-annotation as initialization in the firstframe.”
A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration (2014, methodology): “Success plots of OPE − deformation (19) SAMF [0.624] KCF [0.534] SCM [0.448] DFT [0.439] CN [0.438] Struck [0.393] TLD [0.378] VTD [0.377] LSK [0.377] ASLA [0.372] (c) 0 10 20 30 40 50 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Location error threshold”
Transfer Learning Based Visual Tracking with Gaussian Processes Regression (2014, methodology): “, a fixed prior classifier trained by the labeled samples from the first frame [7], a measurement matrix for compressive sensing [41], a fixed dictionary for histogram generation [45], or utilize the MUG estimation instead of the MAP estimation [19].”
Multi-cue Correlation Filters for Robust Visual Tracking (2018, background): “, sparse representation based methods [57, 18], about 1 FPS), which restricts the real-time application.”